This research has a lot of scope for future explorations. For example, at present there are approaches available to learn feature representations based on static networks. But in reality, networks evolve over time and the topology of a node in multiple layers change over a span of time [1]}. Temporal networks can effectively model data from multiple fields like financial transactions and social media cascades. Another addition to the future work can be considering higher order motifs and graphlets [2]}. All existing network embedding techniques focus on pair wise relationship among nodes, compromising the actual structural values of a network. However, motifs and graphlets by default captures such properties.
